Human intelligence is often understood as a process of turning experience into new behavior, knowledge, and skills. Humans achieve this by constructing small models of the world in their brains to explain the sensory experience and use them to infer new consequences. These models can be understood as turning experience into compact representations: functions of the experienced data which are useful for a given task.
Representation learning [1]} aims to mimic this process with machines, via studying and formalizing what makes a good representation of high dimensional data, and how can we compute it in the form of an algorithm.
A central topic in this research field is disentangled representations, which advocates that a representation of an entity should capture the different latent factors of variation in the world where that entity is observed.
This abstract concept has led to several formalizations, but the community has not yet settled on a common definition.
